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Record W4214765122 · doi:10.14512/rur.132

Leaving, Staying in and Returning to the Hometown

2022· article· en· W4214765122 on OpenAlexaff
Janna Albrecht, Joachim Scheiner

Bibliographic record

VenueRaumforschung und Raumordnung / Spatial Research and Planning · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsTransport Canada
FundersTechnische Universität DortmundEidgenössische Technische Hochschule ZürichDeutsche Forschungsgemeinschaft
KeywordsResidenceSocializationDemographic economicsLife course approachPopulationSociologyImmigrationGeographyPsychologyGender studiesSocial psychologyDemographyEconomics

Abstract

fetched live from OpenAlex

Couples' residential decisions are based on a large variety of factors including housing preferences, family and other social ties, socialisation and residential biography (e.g. earlier experience in the life course) and environmental factors (e.g. housing market, labour market). This study examines, firstly, to what extent people stay in, return to or leave their hometown (referred to as ‘migration type’). We refer to the hometown as the place where most of childhood and adolescence is spent. Secondly, we study which conditions shape a person’s migration type. We mainly focus on variables capturing elements of the residential biography and both partners’ family ties and family socialisation. We focus on the residential choices made at the time of family formation, i.e. when the first child is born. We employ multinomial regression modelling and cross-tabulations, based on two generations in a sample of families who mostly live in the wider Ruhr area, born around 1931 (parents) and 1957 (adult children). We find that migration type is significantly affected by a combination of both partners' place of origin, both partners' parents' places of residence, the number of previous moves, level of education and hometown population size. We conclude that complex patterns of experience made over the life course, socialisation and gendered patterns are at work. These mechanisms should be kept in mind when policymakers develop strategies to attract (return) migrants.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.515
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0060.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.126
GPT teacher head0.434
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2022
Admission routes1
Has abstractyes

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